一、 跨文化表情認知比較分析 (一)心滿意足表情的臉部造形表現關鍵
同樣以心滿意足的意涵做圖片認知的實驗,從偏相關係數值的 大小排序來看,如表 12 所示,兩地受測者在較重要的前三個項目 排序均相同,顯示在兩地文化中,對於心滿意足的表情認知,都是 以嘴型、眉型、眼型的表現關鍵性較高,而瞳孔與頭部旋轉角度,
做任何變化均無很大的影響力,因此,在動畫劇情或其他藝術與設 計情境中,前三個項目的造形有特定的表現方式,而後兩個項目則 可以隨情境所需任意做改變。
表 12 兩地受測結果的偏相關係數值排序比較表 排
序
台灣受測者 舊金山受測者
1 D(嘴型) D(嘴型) 2 A(眉型) A(眉型) 3 B(眼型) B(眼型) 4 C(瞳孔) E(頭部旋轉) 5 E(頭部旋轉) C(瞳孔)
(二)正向表現的差異
本次實驗主題「心滿意足」與「Satisfied」,被研究團隊認為是 文意上清楚明確,在中英文的意涵上可以說非常接近。如果以兩地 受測結果各項目的類目得點數值正向排序,如表 13 所示,除了眉
型項目以外,其他的項目幾乎可說是完全相同。眉型項目的類目排
表 14 兩地受測結果各項目的類目得點數值前三低排序比較表
表 15 最高類目得點繪製兩地受測者認知中的最佳表現圖片
受測者 台灣受測者 舊金山受測者
表情形容詞 心滿意足 Satisfied 眉型最高類目 A5 倒眉下垂 A1 不變 眼型最高類目 B6 閉 B6 閉 瞳孔最高類目 C1 在中心 C1 在中心 嘴型最高類目 D2 左右拉開 D2 左右拉開 頭部旋轉最高類目 E4 歪頭上仰 E4 歪頭上仰
產生圖片
二、 表情認知數值化的優點 (一)得知所有項目對表情的影響程度
如表 10 與表 11 的各偏相關係數,可看出眉毛、眼皮、瞳孔、
嘴唇與頭部旋轉角度的變化,對於人們在臉部表情上的認知,個別 產生多少程度的影響,並了解何者對表情影響最深。
(二)得知所有類目對表情的影響程度
同樣的,由各類目得點可以比較同一個項目中,所有不同類目 的個別表現,例如眼睛項目中的瞇眼、眼睛撐大、一眼閉等,對於 本研究的感性語彙(心滿意足),各有多少比例的影響力,並了解該 項目中何者對表情影響最深。
(三)得知正面影響與負面影響
所有類目得點的數字均帶有正值與負值,正數值代表正向影
響,負數值代表負向影響。我們可以看出各類目與感性語彙之間是 正向或負向的認知關係。
(四)產生預測模式
臉部五官項目之所有類目得點,可進一步分別推演出兩地受測 者在本研究感性語彙的預測模式,即該表情效果等於各最佳類目乘 以類目得點後的加總加上常數項,預測模式已在前述的統計分析中 列出。
(五)產生最佳臉部造形組合
所有以最高得點找出的最佳類目,可以繪製出針對心滿意足的 最佳五官組合,本研究中兩地受測者在心滿意足的最佳表現方式如 表 15 所示。
三、 本研究未來發展與貢獻
我們認為,本研究此次的實驗,屬於跨文化表情認知的初探,
希望能拋磚引玉,未來的發展預期可以有以下貢獻:
(一)商用機器人研發工程師
對商用機器人研發工程師而言,若要使機器人更加人性化,其 中一個要解決的問題,就是在不同的情境下,如何表現出適當的臉 部表情,面對消費者群眾,甚至達到有效的互動情緒。工程師將可 以利用我們的研究結果,設定機器人在不同條件下的臉部變化,甚 至因應不同文化或地域的消費者,同一表情設定不同的造形效果。
(二)動畫設計師
對動畫設計師而言,不論是專業級或是初學者,若能事先獲得
臉部關鍵變化的建議資訊,即能快速創造出所需要人類或擬人化角 色的表情,不需要每次都重複嘗試臉部各關鍵點的位置(Shin & Lee, 2009),以符合劇情或不同地域觀眾群的認知需求。少了這個麻煩,
動畫設計師可以更專注於決定各角色表情誇張的程度,以及不同表 情間轉變的時間點和時間長度等更具創意性的工作上。
(三)戲劇表演者
對戲劇表演者而言,通常心中所想的就會在臉上呈現(McNeil, 2000)。但有時這些是否等同於外表呈現的效果,或不同文化背景觀 眾期待的各種效果,將是另一個議題。此外,經驗不足的初學者有 可能一時之間難以掌握不同臉部表演方式,他們都可以參考本研究 的結果,很快抓住表現重點。
(四)設計或藝術教育工作者
對設計或藝術教育工作者而言,往往對於抽象的事物難以清楚 解釋感到困擾,而臉部表情即是其中之一。不論是表演、動畫、繪 畫、影片製作,只要是牽涉到臉部表情的效果,如果有像本研究的 結果當參考,就可以明確點出學生作品裡臉部五官的改善方式,而 不再是只能說類似「不知道為什麼,這張臉看起來太放鬆,不夠緊 張,該怎麼做自己好好想想」之類建設性不足的評語。
(五)一般人
對某些有以下困擾的人而言,日常生活中,臉上線條往往傳達 錯誤的訊息,造成人與人之間,在沒有口語溝通的情況下產生認知 上的猜疑或誤會,卻不自覺,他們也不知從何改善那些例如看起來 邪惡、刻薄、或是多疑的臉。未來的相關研究,可以擴大需求形容
詞的資料庫,提供一般人參考,試著去修正臉上的線條,幫助人際 關係的改善。
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